Prioritizing Opportunities for Clinical Process Improvement

ABSTRACT

Systems and methods are provided for analyzing opportunities for optimizing clinical processes within clinical facilities. An optimized practice process model may be defined for a particular clinical procedure, setting forth an optimal clinical process. In addition, critical levers may be identified within the optimal clinical process, representing the activities that have the greatest impact on outcomes. The optimized practice process model may also include metrics for quantifying return-on-investments for various opportunities. Clinical facilities may collect clinically-related data, such as current measures for the critical levers. Using the clinically-related data and the optimized practice process model, opportunities for process optimization may be analyzed, for example, by determining a return-on-investment for the various opportunities.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.60/724,982, filed Oct. 7, 2005. Additionally, this application isrelated by subject matter to the inventions disclosed in the followingcommonly assigned applications: U.S. application Ser. No. (not yetassigned) (Attorney Docket Number CRNI.125587), filed on even dateherewith; U.S. application Ser. No. (not yet assigned) (Attorney DocketNumber CRNI.125589), filed on even date herewith; U.S. application Ser.No. (not yet assigned) (Attorney Docket Number CRNI.125590), filed oneven date herewith; U.S. application Ser. No. (not yet assigned)(Attorney Docket Number CRNI.125591), filed on even date herewith; U.S.application Ser. No. (not yet assigned) (Attorney Docket NumberCRNI.127584), filed on even date herewith; and U.S. application Ser. No.(not yet assigned) (Attorney Docket Number CRNI.1257585), filed on evendate herewith. Each of the aforementioned applications is hereinincorporated by reference in its entirety.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not Applicable.

BACKGROUND

Patient treatment from the initial diagnosis until the final patientdischarge may often involve very complex and involved clinicalprocesses. The clinical process for a particular type of treatment mayinclude hundreds of different activities that are performed by a widevariety of actors within the healthcare environment. Because of thecomplexity of some clinical processes, there are often manyopportunities for optimization to improve the quality, delivery, andcost of healthcare. However, the complexity of clinical processes alsooften makes it difficult to identify the opportunities that will havethe greatest impact on improving the outcomes of the processes in anefficient manner.

A number of different approaches have been taken in an attempt toimprove clinical processes within healthcare facilities. For instance,one such approach is transformational consulting. Under this approach,consultants evaluate a clinical facility's current practice for aparticular clinical process. The consultants then attempt to identifyareas within the facility's current clinical process that requireimprovement. Based on those identifications, the consultants thenattempt to develop changes to the clinical process that may beimplemented to improve the process. This may often involve working withthe client to determine “on the fly” what changes are appropriate toaddress the shortcomings of the current clinical process. However, thisconsulting process is an inefficient approach that is time consuming andlabor intensive. Moreover, this approach focuses primarily on thefacility's current clinical process, potentially ignoring manyopportunities for improvement.

Management information systems have also played a role in attempts toimprove clinical processes. These systems allow healthcare personnel tocollect, track, and analyze a wide variety of clinical data fromhealthcare facilities. While the collection and analysis of such datamay be helpful, there are a number of limitations to the flexibility andsophistication of current clinical management systems. For example,although management information systems allow healthcare facilities togather a wide range of data, some systems may not permit modeling orsimulation of the effect of proposed changes to current clinicalprocedures. Other systems that do permit a user to predict or simulateoutcomes from process changes may do so based only on the internallygenerated clinical data sets that are unconstrained by other objectiveguidelines.

To address the shortcomings of many management information systems,evidence-based modeling of clinical operations has been proposed. Underthis approach, effects on outcomes may be evaluated by comparingempirical data accessed from clinical facilities to objective guidelinesor criteria. However, this approach also poses a number of limitations.For instance, the objective guidelines or criteria used are merelyindividual pieces of information that are independent of an entireclinical process. Accordingly, such an approach may fail to account fora change's effect on the entire clinical process, such as any impact toother activities within the process. Further, such systems do notreadily provide the ability to efficiently analyze and prioritizeclinical process improvements.

BRIEF SUMMARY

This summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

Embodiments of the present invention relate to systems, methods, andgraphical user interfaces that provide a comprehensive and adaptiveapproach to optimizing current clinical processes within clinicalfacilities based on optimized practice process models. The types andaspects of clinical processes that may be optimized using embodiments ofthe present invention are not limited to treatment aspects but may alsoaddress financial, administrative, and operational aspects of healthcareprocesses. In embodiments, an optimized practice process model may bedefined for a particular type of clinical process. The optimizedpractice process model may comprise a variety of information to aid inthe identification of opportunities for improving a current clinicalprocess within a clinical facility. In particular, the optimizedpractice process model comprises an optimal process flow defined for thetype of clinical process, detailing the end-to-end activities for theclinical process. In addition, activities within the optimal processflow that have the greatest potential to impact outcomes are identifiedas critical levers, and an optimal measure is defined for each criticallever. Each critical lever or a set of critical levers may represent apotential opportunity for improving a current clinical process within aclinical facility. Those potential opportunities may be characterized asclinical, financial, operational, and/or regulatory opportunities. Datafor quantifying the benefit and effort for adopting each opportunity mayalso be associated with each optimized practice process model, allowingfor the analysis of the various opportunities for process optimization.Further, because the optimized practice process model details theprocess flow, data is readily available to aid in determining andadopting the necessary changes to facilities' current clinicalprocesses.

In operation, current measures for clinical activities correspondingwith the critical levers identified within an optimized practice processmodel may be collected from a current clinical process within a clinicalfacility. The current measures may then be compared against optimalmeasures, benchmark measures, and/or target measures to identify whichareas of potential opportunity defined by the optimized practice processmodel present areas of opportunity to improve the current clinicalprocess within the clinical facility. The identified opportunities maythen be analyzed and prioritized such that the opportunities having thegreatest benefit with the least effort may be adopted by the clinicalfacility first. Those opportunities determined to have the highestpriority may then be adopted and integrated into the facility's process.

Embodiments of the present invention further provide a closed-loopedprocess as a facility's clinical process may be continuously monitoredto identify out-of-tolerance conditions as well as to identify andprioritize further opportunities for improvement. Moreover, theaggregation of data from multiple facilities allows for refinements tobe made to the optimized practice process model based on the widecollection of empirical data.

Accordingly, in one aspect, an embodiment of the present invention isdirect to a method in a clinical computing environment for analyzing areturn-on-investment for one or more opportunities for improving acurrent clinical process within one or more clinical facilities. Themethod includes accessing optimized practice process model data definingone or more opportunities for clinical process improvement based on anoptimal process flow, the optimized practice process model data furtherdefining return-on-investment metrics for quantifying areturn-on-investment for adopting at least one of the opportunities. Themethod also includes accessing clinically-related data for the currentclinical process based on the opportunities defined by the optimizedpractice process model data. The method further includes determining areturn-on-investment for adopting at least one of the opportunitieswithin the current clinical process, the return-on-investment beingdetermined based on the clinically-related data and return-on-investmentmetrics defined by the optimized practice process model data.

In another aspect of the invention, an embodiment is direct to acomputerized system in a clinical environment for facilitating analysisof a return-on-investment for one or more opportunities for improving acurrent clinical process within one or more clinical facilities. Thesystem includes a first interface to a database storing optimizedpractice process model data, the optimized practice process model datadefining one or more opportunities for clinical process improvementbased on an optimal process flow, the optimized practice process modeldata further defining return-on-investment metrics for quantifying areturn-on-investment for adopting at least one of the opportunities. Thesystem also includes a second interface to a data store storingclinically-related data from the current clinical process. The systemfurther includes a knowledge manager communicating with the database viathe first interface and the data store via the second interface, theknowledge manager configured to determine a return-on-investment foradopting at least one of the opportunities within the current clinicalprocess, the return-on-investment being determined based on theclinically-related data and return-on-investment metrics defined by theoptimized practice process model.

In a further aspect, an embodiment of the invention is directed to amethod in a clinical computing environment for analyzing areturn-on-investment for one or more opportunities for improving acurrent clinical process within one or more clinical facilities. Themethod includes accessing optimized practice process model data definingone or more critical levers based on an optimal clinical process, theoptimized practice process model data further associatingreturn-on-investment metrics with each of the critical levers. Themethod also includes accessing one or more current measures for thecurrent clinical process, each of the current measures correspondingwith at least one of the critical levers. The method further includesidentifying one or more opportunities for improving the current clinicalprocess by comparing at least one of the current measures against theoptimized practice process model data. The method still further includesdetermining a return-on-investment for adopting at least one of theopportunities within the current clinical process, thereturn-on-investment being determined based on the current measures andthe return-on-investment metrics defined by the optimized practiceprocess model.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The present invention is described in detail below with reference to theattached drawing figures, wherein:

FIG. 1 is a block diagram of an exemplary computing environment suitablefor use in implementing the present invention;

FIG. 2 is a block diagram showing an exemplary overall systemarchitecture in which clinical system optimization may be performed inaccordance with an embodiment of the present invention;

FIG. 3 is a flow diagram showing an overall method for clinical processoptimization in accordance with an embodiment of the present invention;

FIG. 4 is a flow diagram showing a method for defining an optimizedclinical practice process model in accordance with an embodiment of thepresent invention;

FIG. 5 is a flow diagram showing a method for identifying opportunitiesfor clinical process optimization in accordance with an embodiment ofthe present invention;

FIG. 6 is an illustrative screen display of an exemplary opportunitysummary user interface showing opportunities identified by the knowledgemanager in accordance with an embodiment of the present invention;

FIG. 7 is an illustrative screen display of an exemplary financialbenefits summary user interface showing the financial benefit foridentified opportunities in accordance with an embodiment of the presentinvention;

FIG. 8 is an illustrative screen display of an exemplary opportunitymetrics user interface providing details regarding general areas ofopportunity in accordance with an embodiment of the present invention;

FIG. 9 is an illustrative screen display of an exemplary opportunityvalue user interface displaying whether activities provide a clinical,financial, operational, and/or regulatory opportunity in accordance withan embodiment of the present invention;

FIG. 10 is an illustrative screen display of an exemplary user interfacefor reviewing an optimal clinical process flow in accordance with anembodiment of the present invention;

FIG. 11 is an illustrative screen display of an exemplary priorityanalysis user interface for prioritizing identified opportunities inaccordance with an embodiment of the present invention, wherein allopportunities have been selected for analysis;

FIG. 12 is an illustrative screen display of an exemplary priorityanalysis user interface in accordance with an embodiment of the presentinvention, wherein only a subset of opportunities have been selected foranalysis;

FIG. 13 is an illustrative screen display of an exemplary net changeuser interface for viewing monitoring data in accordance with anembodiment of the present invention;

FIG. 14 is an illustrative screen display of an exemplary problemsummary user interface in accordance with an embodiment of the presentinvention;

FIG. 15 is an illustrative screen display showing monitoring datarelating to a rule violation indicated in the problem summary userinterface in accordance with an embodiment of the present invention;

FIG. 16 is an illustrative screen display allowing review of themonitoring data by physician in accordance with an embodiment of thepresent invention;

FIG. 17 is an illustrative screen display showing alert overrides inaccordance with an embodiment of the present invention;

FIG. 18 is an illustrative screen display showing reasons for alertoverrides in accordance with an embodiment of the present invention;

FIG. 19 is a flow diagram showing a method for monitoring a currentclinical process for variance conditions in accordance with anembodiment of the present invention;

FIG. 20 is a flow diagram showing a method for measuring performanceimprovement for a clinical process within one or more healthcarefacilities in accordance with an embodiment of the present invention;and

FIG. 21 is an illustrative screen display showing performanceimprovements for a selected area of a clinical process in accordancewith an embodiment of the present invention.

DETAILED DESCRIPTION

The subject matter of the present invention is described withspecificity herein to meet statutory requirements. However, thedescription itself is not intended to limit the scope of this patent.Rather, the inventors have contemplated that the claimed subject mattermight also be embodied in other ways, to include different steps orcombinations of steps similar to the ones described in this document, inconjunction with other present or future technologies. Moreover,although the terms “step” and/or “block” may be used herein to connotedifferent components of methods employed, the terms should not beinterpreted as implying any particular order among or between varioussteps herein disclosed unless and except when the order of individualsteps is explicitly described.

Embodiments of the present invention provide computerized methods,systems, and graphical user interfaces for identifying, analyzing, andadopting opportunities for optimizing clinical processes based onoptimized practice process models. Having briefly provided an overviewof the present invention, embodiments of the invention will be discussedwith reference to FIGS. 1-21.

Referring to the drawings in general, and initially to FIG. 1 inparticular, an exemplary computing system environment, for instance, amedical information computing system, on which embodiments of thepresent invention may be implemented is illustrated and designatedgenerally as reference numeral 20. It will be understood and appreciatedby those of ordinary skill in the art that the illustrated medicalinformation computing system environment 20 is merely an example of onesuitable computing environment and is not intended to suggest anylimitation as to the scope of use or functionality of the invention.Neither should the environment 20 be interpreted as having anydependency or requirement relating to any single component orcombination of components illustrated therein.

The present invention may be operational with numerous other generalpurpose or special purpose computing system environments orconfigurations. Examples of well-known computing systems, environments,and/or configurations that may be suitable for use with the presentinvention include, by way of example only, personal computers, servercomputers, hand-held or laptop devices, multiprocessor systems,microprocessor-based systems, set top boxes, programmable consumerelectronics, network PCs, minicomputers, mainframe computers,distributed computing environments that include any of theabove-mentioned systems or devices, and the like.

The present invention may be described in the general context ofcomputer-executable instructions, such as program modules, beingexecuted by a computer. Generally, program modules include, but are notlimited to, routines, programs, objects, components, and data structuresthat perform particular tasks or implement particular abstract datatypes. The present invention may also be practiced in distributedcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed computing environment, program modules may be located inlocal and/or remote computer storage media including, by way of exampleonly, memory storage devices.

With continued reference to FIG. 1, the exemplary medical informationcomputing system environment 20 includes a general purpose computingdevice in the form of a server 22. Components of the server 22 mayinclude, without limitation, a processing unit, internal system memory,and a suitable system bus for coupling various system components,including database cluster 24, with the server 22. The system bus may beany of several types of bus structures, including a memory bus or memorycontroller, a peripheral bus, and a local bus, using any of a variety ofbus architectures. By way of example, and not limitation, sucharchitectures include Industry Standard Architecture (ISA) bus, MicroChannel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronic Standards Association (VESA) local bus, and PeripheralComponent Interconnect (PCI) bus, also known as Mezzanine bus.

The server 22 typically includes, or has access to, a variety ofcomputer readable media, for instance, database cluster 24. Computerreadable media can be any available media that may be accessed by server22, and includes volatile and nonvolatile media, as well as removableand non-removable media. By way of example, and not limitation, computerreadable media may include computer storage media and communicationmedia. Computer storage media may include, without limitation, volatileand nonvolatile media, as well as removable and nonremovable mediaimplemented in any method or technology for storage of information, suchas computer readable instructions, data structures, program modules, orother data. In this regard, computer storage media may include, but isnot limited to, RAM, ROM, EEPROM, flash memory or other memorytechnology, CD-ROM, digital versatile disks (DVDs) or other optical diskstorage, magnetic cassettes, magnetic tape, magnetic disk storage, orother magnetic storage device, or any other medium which can be used tostore the desired information and which may be accessed by the server22. Communication media typically embodies computer readableinstructions, data structures, program modules, or other data in amodulated data signal, such as a carrier wave or other transportmechanism, and may include any information delivery media. As usedherein, the term “modulated data signal” refers to a signal that has oneor more of its attributes set or changed in such a manner as to encodeinformation in the signal. By way of example, and not limitation,communication media includes wired media such as a wired network ordirect-wired connection, and wireless media such as acoustic, RF,infrared, and other wireless media. Combinations of any of the abovealso may be included within the scope of computer readable media.

The computer storage media discussed above and illustrated in FIG. 1,including database cluster 24, provide storage of computer readableinstructions, data structures, program modules, and other data for theserver 22.

The server 22 may operate in a computer network 26 using logicalconnections to one or more remote computers 28. Remote computers 28 maybe located at a variety of locations in a medical or researchenvironment, for example, but not limited to, clinical laboratories,hospitals and other inpatient settings, veterinary environments,ambulatory settings, medical billing and financial offices, hospitaladministration settings, home health care environments, and clinicians'offices. Clinicians may include, but are not limited to, a treatingphysician or physicians, specialists such as surgeons, radiologists,cardiologists, and oncologists, emergency medical technicians,physicians' assistants, nurse practitioners, nurses, nurses' aides,pharmacists, dieticians, microbiologists, laboratory experts, geneticcounselors, researchers, veterinarians, students, and the like. Theremote computers 28 may also be physically located in non-traditionalmedical care environments so that the entire health care community maybe capable of integration on the network. The remote computers 28 may bepersonal computers, servers, routers, network PCs, peer devices, othercommon network nodes, or the like, and may include some or all of thecomponents described above in relation to the server 22. The devices canbe personal digital assistants or other like devices.

Exemplary computer networks 26 may include, without limitation, localarea networks (LANs) and/or wide area networks (WANs). Such networkingenvironments are commonplace in offices, enterprise-wide computernetworks, intranets, and the Internet. When utilized in a WAN networkingenvironment, the server 22 may include a modem or other means forestablishing communications over the WAN, such as the Internet. In anetworked environment, program modules or portions thereof may be storedin the server 22, in the database cluster 24, or on any of the remotecomputers 28. For example, and not by way of limitation, variousapplication programs may reside on the memory associated with any one ormore of the remote computers 28. It will be appreciated by those ofordinary skill in the art that the network connections shown areexemplary and other means of establishing a communications link betweenthe computers (e.g., server 22 and remote computers 28) may be utilized.

In operation, a user may enter commands and information into the server22 or convey the commands and information to the server 22 via one ormore of the remote computers 28 through input devices, such as akeyboard, a pointing device (commonly referred to as a mouse), atrackball, or a touch pad. Other input devices may include, withoutlimitation, microphones, satellite dishes, scanners, or the like.Commands and information may also be sent directly from a remotehealthcare device to the server 22. In addition to a monitor, the server22 and/or remote computers 28 may include other peripheral outputdevices, such as speakers and a printer.

Although many other internal components of the server 22 and the remotecomputers 28 are not shown, those of ordinary skill in the art willappreciate that such components and their interconnection are wellknown. Accordingly, additional details concerning the internalconstruction of the server 22 and the remote computers 28 are notfurther disclosed herein.

Having described an exemplary computing system environment, an exemplaryoverall system architecture 200 in which embodiments of the presentinvention may be employed is shown in FIG. 2. The overall systemarchitecture 200 may include a number of clinical facilities, such asthe clinical facilities 202, 204, 206, a data warehouse 208, a knowledgemanager 210, and an optimized practice process model database 212. Theoverall system architecture 200 shown in FIG. 2 is illustrative, andmodifications in configuration and implementation will occur to personsskilled in the art. For instance, while the overall system architecture200 is shown with only a single knowledge manager 210, in embodiments,multiple components may be employed independently or together to analyzeopportunities for clinical process optimization within clinicalfacilities. Likewise, in various embodiments, more than one datawarehouse and optimized practice process model database may be employed.Further, components shown separately within FIG. 2 may be combined inembodiments of the present invention.

The overall system architecture 200 shown in FIG. 2 provides a systemthat may be employed to identify and analyze opportunities or objectivesto improve clinical processes within a clinical facility or set ofclinical facilities (e.g., a collection of clinical facilities within ahealthcare system). The opportunities often address healthconsiderations within a clinical process. Opportunities for processoptimization may be identified by comparing current measures from acurrent clinical process within a clinical facility against an optimizedpractice process model for the particular type of clinical process beinganalyzed.

The optimized practice process model database 212 may store one or moreoptimized practice process models, each of which contains data relatingto an optimal clinical process. Each optimal clinical process detailsthe activities required within the end-to-end process flow, includingthe actors and venues required to accomplish each activity. By definingan optimal clinical process, embodiments of the present inventionrecognize and account for interrelationships between activities within aprocess flow, thereby providing a significant advantage over otherapproaches in which individual pieces of evidence are used in isolationof an overall end-to-end process.

The optimal clinical process may be defined based on a variety ofdifferent data within the scope of the present invention. Typically,available literature and best published evidence (e.g., medical,clinical, operational, and other guidelines, trade magazines, and thelike) may be used to define the optimal clinical process. In addition,operational evidence collected from a variety of facilities (such asthat stored in the data warehouse 208 described in further detailbelow), may be used to define the optimal clinical process. One skilledin the art will recognize that a variety of other data may also be usedwithin the scope of the present invention.

Within each optimal clinical process, activities that have the greatestimpact on outcomes are identified as critical levers within the data. Inother words, the critical levers represent those activities that presentthe greatest opportunities for optimizing the clinical process. Anoptimal measure is also identified for each critical lever andassociated with each critical lever within the optimized practiceprocess model database 212. Similar to defining the optimal clinicalprocess, identification of the critical levers and an optimal measurefor each critical lever may be based on best published evidence,available operational data, and other clinically-related data that mayaid in the identification of best practices. Because the reliability ofsuch information varies widely, the credibility of the source ofinformation may also be included in the optimized practice processmodel.

Each critical lever or a set of critical levers may represent apotential opportunity for clinical process optimization. Accordingly,information related to the opportunity for clinical process improvementfor each critical lever or set of critical levers may also be definedand stored within the optimized practice process model database 212.Generally, the critical levers may represent clinical, regulatory,operational, and/or financial opportunities. In addition,return-on-investment (or performance improvement) metrics may be definedwithin the optimized practice process model for determining areturn-on-investment for implementing each opportunity to allowprioritization of opportunities. The return-on-investment metrics mayinclude benefit metrics for determining a benefit for adopting anopportunity. The benefit metrics may include data to allow for thequantification of both financial and non-financial benefits of eachopportunity. In addition, the return-on-investment metrics may includeeffort metrics for quantifying an effort for adopting each opportunity.Further, because the optimal clinical process within an optimizedpractice process model details the end-to-end activities of a particularclinical process, the models contain data regarding the changesnecessary to adopt opportunities.

The optimized practice process model database 212 may be incommunication with the knowledge manager 210, which may be employed toperform opportunity identification and analysis. The knowledge manager210 may likewise be in communication with a source of data relating toone or more clinical facilities. In particular, the knowledge manager210 may access a clinical facility's current measures for activitiescorresponding with critical levers defined by an optimized practiceprocess model, and may compare those current measures with other definedmeasures, such as an optimal measure, a benchmark measure (based onmeasures from a collection of clinical facilities), and/or a targetmeasure that has been defined for the clinical facility. The definedmeasures may be accessed from the optimized practice process modeldatabase, the data warehouse, and/or another associated database.Through the comparison, opportunities for process optimization for theclinical facility may be identified. The knowledge manager 210 furthergenerates a number of graphical user interfaces to allow a user toanalyze the identified opportunities and determine which opportunitiesto adopt and integrate into a current clinical process.

The knowledge manager 210 may access data regarding a clinical facilityfrom the clinical facility itself or from a data warehouse, such as thedata warehouse 208, which may store data from a number of differentclinical facilities. Each clinical facility may be, for example, ahospital, clinic, research site, corporate facility, government ormilitary site, or other facility that conducts medically-relatedoperations. A clinical facility may have the ability to collect andcondition captures of clinically-related data, including currentmeasures for critical levers. In some cases, a database may beassociated with a clinical facility for storing the clinically-relateddata, such as the databases 214, 216, and 218. Additionally, in somecases, a database may be associated with and store data for multipleclinical facilities. Each clinical facility may further communicate theclinically-related data to the knowledge manager 210 and/or the datawarehouse 208. In addition to current measures for critical levers, theclinically-related data may include, for example, a variety of medical,financial, operational, administrative, and other information,including, for instance, sets of patient identification data, diagnosisdata, patient morbidity, mortality and recovery rates, drug prescriptionand other drug delivery and management information, hospital or otheroccupancy data, revenue streams by department or facility, supply andcapital cost information, medical staff information, schedulinginformation, or other types of information related to clinicaloperations.

The data warehouse 208 may collect and store clinically-related data,including current measures for critical levers, from multiple clinicalfacilities. The collection of data from multiple facilities may providea number of advantages. For example, a benchmark measure for criticallevers may be determined based on the provided data. Such benchmarkmeasures may permit facilities to compare their performance againsttheir peers. In addition, the collection of data may be used for variousother analytic purposes. For example, if a particular facility isoutperforming other facilities, its clinically-related data may becompared against its peers to determine why the facility isoutperforming. Further, the collection of data may be used to improvethe optimized practice process models. For example, the monitored datamay indicate an optimal measure for a particular critical lever orsuggest changes in the optimal clinical process.

Referring to FIG. 3, a flowchart is provided illustrating an exemplaryoverall process flow 300 for improving a current clinical process withinone or more healthcare facilities in accordance with embodiments of thepresent invention. Generally, the overall method may be referred to as aclosed-loop process that allows for the continuous improvement andrefinement of clinical processes within clinical facilities. As shown atblock 302, an optimized practice process model is defined for aparticular type of clinical process. As discussed previously, anoptimized practice process model contains data relating to what may beconsidered as an optimal procedure for a particular type of clinicalprocess.

An exemplary method 400 for defining an optimized practice process modelmay be described with reference to FIG. 4. Initially, an optimalclinical process is determined for the particular type of treatment, asshown at block 402. As previously described, the optimal clinicalprocess details the activities required within the end-to-end processflow, including the actors and venues required to accomplish eachactivity. Determination of the optimal clinical process may be based ona number of different sources. Typically, available literature and bestpublished evidence (e.g., medical, clinical, operational, and otherguidelines, trade magazines, and the like) may be used to define theoptimal clinical process. In addition, operational evidence collectedfrom a variety of facilities may be used to determine the optimalclinical process. After defining the optimal clinical process, thecritical levers within that process are identified, as shown at block404. The critical levers represent those activities within the processthat, if varied, may have the greatest impact on outcomes.

A variety of data may be associated with each of the critical levers.For example, as shown at block 406, an optimal measure for each of theidentified critical levers may be determined. The optimal measure may bebased on best published evidence, available operational data, and otherclinically-related data that may aid in the identification of bestpractices. Because the reliability of such information varies widely,the credibility of the source of information may also be included withthe optimal measure for each critical lever.

Potential opportunities for clinical process improvement are nextdefined based on the critical levers, as shown at block 408. In someembodiments, each critical lever comprises a potential opportunity forclinical process improvement. In other embodiments, sets of criticallevers define potential opportunities. Generally, each critical levermay be described as a clinical, financial, operational, and/orregulatory opportunity. In addition, data allowing for thequantification of the benefit and effort of each opportunity may beassociated with each critical lever, as shown at block 410. This dataallows each opportunity to be analyzed and prioritized based on bothfinancial and non-financial considerations. The data may includereturn-on-investment metrics, including benefit metrics and effortmetrics, for quantifying a return-on-investment to adopt an opportunity.

Referring again to FIG. 3, clinically-related data may be monitored andcollected from a current clinical process within a clinical facility, asshown at block 304. In particular, the data monitored and collectedincludes current measures for activities corresponding with criticallevers identified for the particular type of clinical process underreview as defined within the optimized practice process model. Using themonitored data (in particular, the current measures associated with thecritical levers) and the optimized practice process model for theparticular clinical process, opportunities for process improvement maybe identified, as shown at block 306. An exemplary method foridentifying opportunities using a knowledge manager, such as theknowledge manager 210 of FIG. 2, may be described with reference to FIG.5. As shown at block 502, the knowledge manager may access optimizedpractice process model data (e.g., from an optimized practice processmodel database, such as the optimized practice process model database212 of FIG. 2) for the particular type of clinical process under review.In addition, the knowledge manager may access the clinical facility'scurrent measures for the critical levers identified within the optimizedpractice process model, as shown at block 504. The knowledge manager mayaccess the current measures, for example, from the clinical facility orfrom a common data warehouse, such as the data warehouse 208 of FIG. 2.

The current measures from the clinical facility may next be comparedagainst an optimal measure, a benchmark measure, and/or a targetmeasure, as shown at block 506. The optimal measure for a critical leveris the measure that is considered to be the ideal level for optimizingthe clinical process. The benchmark measure represents the level atwhich other clinical facilities are operating (e.g., the average measureof other clinical facilities) to allow a clinical facility to determinehow it is operating in comparison with its peers. The benchmark measuremay be determined by accessing data contained within the data warehouse.In some embodiments, the benchmark measure may be based on data from allavailable clinical facilities. In other embodiments, the benchmarkmeasure may be based only on a subset of the clinical facilitiesproviding data. For example, a clinical facility may wish to compare itscurrent measures against only similarly situated clinical facilities(e.g., based on size, type, region, etc.). Finally, the target measurefor a critical lever represents a goal level that has been set for theclinical facility. For instance, because the optimal measure and/orbenchmark measure may be difficult for a clinical facility to obtain,the facility may wish to set a goal for analyzing opportunities forimprovement as well as monitoring its progress.

Based on the comparison of the current measure for the clinical facilityagainst an optimal measure, benchmark measure, and/or target measure foreach critical lever, the knowledge manager may identify opportunitiesfor clinical process optimization, as shown at block 508. Essentially,through the comparison, the knowledge manager may identify whichpotential opportunities within the optimized practice process model datapresent areas of opportunity to improve the current clinical processwithin the healthcare facility. To provide for the analysis of theidentified opportunities, the knowledge manager may also generate anumber of graphical user interfaces, as shown at block 510. Thegraphical user interfaces may be generated using data from the optimizedpractice process model for the clinical process under review, includingdata, such as return-on-investment metrics, allowing for thequantification of the benefits and efforts associated with eachopportunity.

Turning back to FIG. 3, after identifying opportunities for processoptimization, the various identified opportunities may be analyzed, asshown at block 308. As mentioned above, the knowledge manager mayprovide a number of graphical user interfaces that a user may navigateto examine the various opportunities. The interfaces may allow the userto view the identified opportunities, as well as a variety of differentaspects of the opportunities, for example, the activities/criticallevers with which the opportunities are associated and their locationwithin the optimal clinical process flow, the various measures for thecritical levers (e.g., the current measure, optimal measure, benchmarkmeasure, and/or the target measure), the type of opportunity (clinical,financial, operational and/or regulatory), the financial benefits of theopportunities, and the return-on-investment for the opportunities.

Using the graphical user interfaces provided by the knowledge manager, auser may prioritize the various opportunities and determine whichopportunities to adopt. Based on that determination, the selectedopportunities may be adopted and integrated into the current clinicalprocess for the clinical facility, as shown at block 310. Because theoptimized practice process model includes detailed information regardingthe optimal clinical process, the model provides information regardinghow to integrate the opportunities (e.g., changes required, actors andvenues involved, etc.)

As mentioned previously, embodiments of the present invention provide aclosed-loop approach to continuously improve the clinical processes ofclinical facilities. Accordingly, as illustrated in FIG. 3, the processtypically does not end with the adoption of selected opportunities.Instead, the clinical facility's operations are continuously monitored,as shown by the return to block 304, to allow for the identification andevaluation of out-of-tolerance conditions, as well as identifying andanalyzing further opportunities for process optimization by repeatingthe process described with reference to block 304 through 310.Typically, a clinical facility may have the resources or ability toadopt only a subset of all identified opportunities at a given time.Accordingly, the process of identifying, analyzing, and adoptingopportunities may be continuously repeated as appropriate for thefacility.

As further represented in FIG. 3, by continuously monitoring andcollecting data from multiple facilities, as well as evaluating theactual success of adopted opportunities, the optimized practice processmodel may be refined, allowing for further clinical processoptimization. For example, the collected data may be used to eitherconfirm or contradict existing information (publication, guideline,empirical data, etc.) that was used to define a particular portion ofthe optimal clinical process and/or used to set an optimal measure for acritical lever. In addition, the collected data may be used to defineportions of the model in which no information is currently available ormay prompt further research and clinical trials. Further, if oneclinical facility is determined to be outperforming its peers, the datamay be evaluated to determine why the facility is outperforming, and theoptimized practice process model may be accordingly refined based onthat evaluation

As discussed previously, the knowledge manager may identifyopportunities to optimize a current clinical process within a healthcarefacility based on an optimized practice process model and may generategraphical user interfaces to allow a user to analyze and prioritizethose opportunities. FIG. 6 through FIG. 12 are illustrative of userinterfaces for reviewing and analyzing opportunities for processoptimization. Although the user interfaces shown in FIG. 6 though FIG.12 show opportunities as sets of clinical levers, as noted previously,in some embodiments, each critical lever may represent an individualopportunity. Accordingly, in such embodiments, the user interfaces maylikewise allow for the analysis of opportunities comprising individualcritical levers. In addition, although the user interfaces shown in FIG.6 through FIG. 12 include opportunities for a single clinical facility,in some embodiments, user interfaces may be provided allowing for theanalysis of opportunities identified for multiple facilities.

Referring initially to FIG. 6, an illustrative screen display 600 isprovided showing an opportunity summary view in accordance with anembodiment of the present invention. The opportunity summary viewprovides an overview of the areas of opportunity identified by theknowledge manager for the current clinical process under review.Generally, the summary view may display each of the potentialopportunities defined by the optimized practice process model and anindication as to whether each potential opportunity was identified aspresenting an area of opportunity to improve the current clinicalprocess under review.

As shown in the screen display 600, the opportunities identified by theknowledge manager may be summarized according to area of analysis 602and venue 604. An indicator icon is provided showing each as an area ofopportunity 606, an area of possible opportunity 608, that the client ismeeting the measure 610, or that not enough information is available612. No indicator icon for a particular area in the summary view (e.g.,the blank area under the “Quality Care” area of analysis for the“Ambulatory” venue) indicates that the particular area was not studied(e.g., some clinical processes may not involve one or more venues). Thescreen display 600 may also include a data area 614, which may displayadditional data regarding the summary view, such as an identification ofthe clinical facility, the time period for analysis, and the study groupvolume.

A financial benefits summary view, such as that shown in the screendisplay 700 of FIG. 7, may also be provided. As shown in FIG. 7, thefinancial benefits summary view indicates the financial benefit that maybe realized if a general area of opportunity is adopted and integratedinto the facility's current clinical process. The financial benefits foreach opportunity may be calculated based on financial data provided inthe optimized practice process model, as well as the comparison ofcurrent measures against optimal, benchmark, and/or target measures.

Further details regarding a general area of opportunity may be viewed bynavigating to an opportunity metrics interface. In some embodiments, forinstance, each general area of opportunity within the screen display 600and the screen display 700 may have an embedded link to allow users toselect an area and view details. For example, if a user were to selectthe indicator icon 616 for the “Safety/Risk Management” area of analysisunder the “Ambulatory” venue, an interface, such as that shown in thescreen display 800 of FIG. 8, may be presented to the user. The screendisplay 800 illustrates an opportunity metrics interface providing avariety of details regarding the “Safety/Risk Management—Ambulatory”area of opportunity 802. A user may also view details of other generalareas of opportunities by using a drop down menu 804 provided within theinterface.

Each general area of opportunity may have a number of activities fromthe optimal clinical process associated with it. These activitiesrepresent the critical levers for the particular area of opportunitybeing viewed. For example, as illustrated in FIG. 8, five activitieshave been associated with the “Safety/Risk Management—Ambulatory” areaof opportunity 802. In addition, the activities may be grouped withinthe area of opportunity, such as the three groupings shown in the screendisplay 800: “Hb Management,” “Infection Prevention,” and “MedicalClearance.”

For each activity, a description of the measurement 806 for the activityis provided, as well as the current measure 808, benchmark measure 810,optimal measure 812, and target measure 814 associated with thatmeasurement. An indicator icon 816, similar to those used in the screendisplay 600 of FIG. 6, is also provided to indicate whether theparticular activity presents an opportunity for process optimization.For example, for the activity labeled “2.15.6.1.16 Consider Type &Screen” 818, the measurement is the percentage of patients for which ablood type and screen is performed. As shown in FIG. 8, the clinicalfacility is currently performing a blood type and screen for only 46% ofits patients, while the optimal, benchmark, and target measures are all100%. Accordingly, the activity has been indicated as area ofopportunity.

An effort index 820, representing a quantification of the effort toadopt an opportunity, may also be provided for the various opportunitiesto allow further analysis and prioritization as will be described infurther detail below. As shown in the screen display 800, each groupingwithin the general area of opportunity has been assigned an effortindex. In some embodiments, an effort index may be displayed forindividual activities, while in other embodiments, an effort index maybe displayed for the general area of opportunity. Each effort index maybe determined based at least in part on effort metrics defined withinthe optimized practice process model.

An annual financial benefit may also be calculated for each opportunityand displayed to the user. In the screen display 800, for example, anannual financial benefit is shown for each grouping of activities. Thefinancial benefit for each activity may be determined by comparing thecurrent measure against one of the benchmark measure, the optimalmeasure, and the target measure for that activity and applying financialbenefit metrics from the optimized practice process model. For example,a clinical facility may have a current measure for a particular activityof 75%, while the optimal measure is 100%. If the clinical facilityhandles 1000 cases annually and the cost benefit associated with theactivity is $100 per case, the clinical facility may realize an annualbenefit of $25,000 by achieving the optimal measure for the activity.

As discussed with respect to the effort index, in some embodiments, anannual financial benefit may be displayed for each activity, while inother embodiments, an annual financial benefit may also be displayed forthe general area of opportunity. In addition, the financial benefit foreach opportunity may be determined based at least in part on benefitmetrics defined within the optimized practice process model. It shouldbe noted that, as indicated for the “HB Management” grouping, an annualfinancial benefit may be a negative amount. This reflects that someopportunities may require changes that would cause the facility to incuradditional costs, but the clinical, operational, and/or regulatorybenefits may outweigh the financial cost. Additionally or alternatively,adoption of the opportunity may provide a benefit that is realizedwithin one or more other activities within the clinical process flowjustifying or offsetting the cost.

A user may also view the value of each activity within a general area ofopportunity by navigating to an opportunity value interface. Forexample, the screen display 900 illustrated in FIG. 9 provides anopportunity value interface for the “Safety/Risk Management—Ambulatory”area of opportunity. The user interface indicates whether each activityrepresents a clinical opportunity 902, a regulatory opportunity 904, anoperational opportunity 906, and/or a financial opportunity 908. Forexample, as shown in FIG. 9, the activity labeled “2.15.6.1.16ConsiderType & Screen” 910 presents a clinical, operational, and financialopportunity for process optimization.

A user may also wish to view the optimal clinical process and, moreparticularly, the location of a particular activity within that optimalprocess flow. Accordingly, the user may navigate to an interface for theoptimal clinical process. In some embodiments, activities, such as thoseshown in either the screen display 800 of FIG. 8 or the screen display900 of FIG. 9, may each have an embedded link to the optimal processflow that may be selected to view the process flow interface. Forexample, if a user were to select the activity labeled “2.15.6.1.16Consider Type & Screen,” the screen display 1000 shown in FIG. 10 may bepresented to the user. As shown in FIG. 10, the embedded link may bringthe user directly to the specific location of the selected activity 1002within the optimal process flow. The user may then scroll through theoptimal clinical process and view the various activities. In someembodiments, an indication, such as coloring of the activity or thedisplay of a tag with the activity, for instance, may be provided toindicate those activities that have been designated as a critical leverand whether those activities have been identified as an area ofopportunity or otherwise. Further, in some embodiments, each activitymay have an embedded link that allows a user to navigate back to anotherinterface, such as the opportunity metrics or value interfaces of FIG. 9and 10, for example.

Referring now to FIG. 11, a screen display 1100 is provided showing apriority analysis user interface for further analyzing opportunities forprocess optimization. A user may employ the priority analysis userinterface to evaluate the return-on-investment afforded by eachopportunity identified by the knowledge manager and prioritize thoseopportunities for adoption. The return-on-investment for eachopportunity may be based on return-on-investment metrics, includingbenefit metrics and effort metrics, defined within the optimizedpractice process model. As shown in FIG. 11, the priority analysis userinterface may include a summary table 1102, a priorities chart 1104, atotal benefit table 1106, and an assumptions area 1108.

The summary table 1102 lists the various opportunities that have beenidentified and provides summary information for each opportunity.Typically, the summary table 1102 will include those areas identified aseither an area of opportunity or an area of possible opportunity, and anindicator icon may be provided for each. As shown in FIG. 11, thesummary information may include an identification of each opportunity(e.g., the opportunities are identified by an associated “Area ofAnalysis” 1110 and “Venue” 1112), the financial benefits 1114, a benefitindex 1116, and an effort index 1118. It should be noted that theinformation provided in the summary table 1102 is illustrative only andother information may be provided within the scope of the presentinvention.

The benefit index and effort index for each opportunity provide aconvenient approach for comparing and prioritizing the opportunities.The benefit index quantifies the financial and non-financial benefits(e.g., clinical, financial, operational, and regulatory benefits) ofeach opportunity. The benefit index may be determined based on aweighted average of two factors. The first factor of the benefit indexis based on the financial benefit of each opportunity, while the secondfactor is based on the “soft” benefits (e.g., clinical, operational, andregulatory benefits) that may present non-financial processimprovements. To determine the financial factor of the benefit index,the opportunities are ranked based on financial benefits, and a relativevalue between zero and ten is assigned to each opportunity based on itsrank. The soft benefits factor of the benefit index is based onsubjective values assigned to each opportunity. These values may bepre-determined and defined within the optimized practice process modelas the benefit metrics for each clinical process. The financial andnon-financial factors may then be weighted and combined to determine thebenefit index for each opportunity.

The effort index represents the ease or difficulty of changes requiredto adopt and integrate a particular opportunity into a facility'sclinical process. It is a relative measure that is subjectively assignedto each opportunity. Similar to the measures for the non-financialbenefits, the effort measures for each opportunity may be based onvalues that are pre-determined and defined within the optimized practiceprocess model as effort metrics for each clinical process.

Opportunities may be displayed within the priorities chart 1104 based ontheir respective benefit index and effort index. Accordingly, the chartprovides a visual representation of the return-on-investment for eachopportunity, such that a user may readily identify those opportunitiesthat will have the greatest impact on outcomes at the least amount ofeffort. Using the priorities chart, a user may prioritize the variousopportunities and determine which opportunities to adopt.

As shown in FIG. 11, the priorities chart 1104 may be described ashaving three value zones: a higher value zone 1120, a middle value zone1122, and a lower value zone 1124. Opportunities displayed in the highervalue zone offer a greater value as they provide the greatest benefit atthe least amount of effort. Opportunities in middle and lower value zonehave a lower relative value as they provide benefit at a greaterrelative effort. By viewing the priorities chart 1104, a user may beable to readily determine which opportunities to adopt. For example, auser may choose to adopt only those opportunities within the highervalue zone.

The total financial benefits for the identified opportunities aresummarized in the total benefit table 1106. As shown in the screendisplay 1100 of FIG. 11, the total benefit table 1106 may include avariety of financial information to aid a user in determining thepresent and future value of adopting the opportunities.

The assumptions area 1108 of the priority analysis user interfacedetails a variety of assumptions used in the process. For example, theassumptions area 1008 shown in FIG. 11 provides information relating toa number of assumptions, including the “Average Reimbursement per case,”“Average Cost per case,” “Average labor rate,” and “Discounted Cash FlowRate.” In some embodiments, the assumptions may be user-adjusted bychanging a value within the priority analysis user interface andclicking on an update button 1126. It should be noted that theassumptions shown in the screen display 1100 are illustrative only, anda variety of additional assumptions may be provided within the scope ofthe present invention.

In some embodiments of the present invention, the weighting applied tothe financial and non-financial factors within the benefit index mayalso be user-adjusted. For example, the priority analysis user interfaceshown in the screen display 1100 provides a weighting input portion 1128that allows a user to adjust the “Clinical Benefit Weight” (i.e. theweighting for the non-financial, soft benefits). After inputting adesired value in the weighting input portion 1128, the user may click onthe update button 1126 to update the benefit indices and thecorresponding location of the opportunities within the priorities chart1104. Accordingly, a user may adjust the financial and non-financialcontributions to the benefit indices to further analyze the variousopportunities depending upon user-preferred outcomes. For example, auser may be primarily interested in realizing financial benefits and maydecrease the clinical benefit weight to determine the opportunities thathave the greatest financial return on investment. Alternatively, a usermay be primarily interested in non-financial benefits (e.g., clinical,operational, and regulatory benefits) and may increase the clinicalbenefit weight such that the benefit indices better reflect theimportance of those soft benefits.

Further, in some embodiments of the present invention, the opportunitiesincluded in the priorities chart 1104 and used to determine the totalbenefit displayed in the total benefit table 1106 may be user-adjusted.For example, as shown in the screen display 1100, the user interface hasan “Include” indication 1130 within the summary table 1102. By clickingon the box corresponding with a particular opportunity, a user maychoose whether to include the opportunity. For instance, in the screendisplay 1100 of FIG. 11, all opportunities have been selected to bedisplayed in the priorities chart 1104 and used to calculate the totalbenefit. If a user wished to evaluate only a subset of the totalopportunities, the user may unselect opportunities and click on theupdate button 1126. For example, the screen shot 1200 of FIG. 12illustrates the priorities analysis user interface if only the firstfour opportunities have been selected in the opportunity summary table1202. As shown in FIG. 12, only those four selected opportunities aredisplayed on the priorities chart 1202. In addition, the values withinthe total benefit table 1204 are updated to reflect only those fouropportunities.

The priorities analysis user interface shown in FIG. 11 and 12 mayfurther include embedded links to other user interfaces. For example,each of the indicator icons displayed on the priorities chart 1104 andin the opportunities summary table 1102 may have an embedded link to auser interface providing more detailed information regarding thecorresponding opportunity (e.g., the user interface shown in the screendisplay 800 of FIG. 8).

Although the screen displays 1100 and 1200 of FIG. 11 and FIG. 12,respectively, illustrate a priority analysis user interface in whichgeneral areas of opportunity comprising sets of critical levers areanalyzed, in various embodiments of the present invention, the priorityanalysis user interface may be used to analyze opportunities at varyinglevels. For example, as indicated previously, in some embodiments,opportunities may be analyzed at the individual critical lever oractivity level.

As described previously, because embodiments of the present inventionprovide a closed-loop process for continuously improving clinicalprocesses, monitoring of data from clinical facilities typicallycontinues after opportunities have been adopted. The continuousmonitoring allows for further refinement of the clinical processes, aswell as the determination of variance (i.e. out-of-tolerance)conditions. Accordingly, embodiments of the present invention alsoinclude systems, methods, and graphical user interfaces for reviewingmonitoring data collected from clinical facilities. FIG. 13 through FIG.18 are illustrative of user interfaces that may be employed to reviewthe monitoring data. The user interfaces may allow a user to identifyvariance conditions and manage efforts to determine the root cause ofthe condition and to decide whether any attempts to correct thecondition should be pursued.

Referring initially to FIG. 13, a screen shot 1300 of a user interfacefor reviewing net changes in operation is provided. As shown in FIG. 13,the user interface may include an action list 1302, a watch list 1304,and an improvement list 1306. The action list 1302 includes areas thatare indicated as areas of opportunity, the watch list 1304 includesareas that are indicated as possible areas of opportunity, and theimprovement list 1306 includes areas in which the measurement iscurrently being met. The user interface may also provide other summaryinformation, such as the client name 1308, facility 1310, service line1312, area of analysis 1314, indicator 1316, previous indicator 1318,date changed 1320, and the status 1322. The areas presented in the userinterface may be filtered to focus on specific areas, for example, byusing the drop down menus 1324 shown in the screen shot 1300.

A succession of user interfaces may be provided to navigate variousdetails of a particular area. For example, the screen shot 1400 of FIG.14 illustrates an exemplary problem summary user interface for aselected area. The problem summary user interface may provide varioussummary information regarding variance conditions identified by thesystem. For example, the screen shot 1400 provides information includingthe measurement 1402 (i.e. % APT Usage) of interest, as well as acurrent value 1404, last value 1406, value last month 1408, mean 1410,and standard deviation 1412 for that measurement. In addition, a ruleviolation indication 1414 may be provided to indicate a rule that hasbeen violated for the measurement. For example, the “5 Down” indication1416 represents that there have been five consecutive declines in thevalue. As further illustrated in FIG. 14, the problem summary userinterface may also be used to manage the condition. For example, a usermay insert notes regarding the nature of the problem and any actionsbeing taken to remedy the condition and may indicate the status of theselected area.

A user may view additional information regarding the rule violation totry to determine the root cause of the condition. For example, a usermay select the rule violation in FIG. 14 (e.g., by clicking on the “5Down” indication which may contain an embedded link), and the interfaceshown in the screen display 1500 of FIG. 15 may be provided. The screendisplay 1500 provides a chart indicating the facility's measure for APTusage percentage over the past year. By reviewing the chart, the userwill readily recognize the decline in the measure.

By selecting the “Review by Physician” link 1502, the user may navigateto the user interface shown in the screen display 1600 of FIG. 16. Asillustrated in FIG. 16, measures are provided at the individualphysician level. Accordingly, the user may identify physicians who aredeviating from optimal, benchmark, and/or target measures. With thatknowledge, in some cases, the user may wish to contact the physicians todetermine reasons for the deviations.

A user may also navigate to an alert overrides user interface, such asthat shown in the screen display 1700 of FIG. 17. As shown in FIG. 17,the percent of alert overrides for the measurement may be provided atthe individual physician level. A user may further review the alertoverrides, for example, by selecting the “Review Alert Overrides” link1702. As illustrated in the screen display 1800 of FIG. 18, the reasonsfor APT override may be provided. In reviewing the screen display 1800,the user may review the reasons provided for deviating from the measureand determine if any remedial action is required. In some cases, thedeviations may require action to address the problem condition, while inother cases, the deviations may prompt a change in the optimal clinicalprocess or defined measures for critical levers (e.g. the optimal and/ortarget measures).

Referring now to FIG. 19, a flow diagram is provided illustrating anexemplary method 1900 for monitoring a current clinical process forvariance conditions in accordance with an embodiment of the presentinvention. The process may begin at block 1902 when a knowledge manageraccesses a rule for a variance condition. A variety of rules forvariance conditions corresponding with critical levers and/oropportunities defined within an optimized practice process model may beused within embodiments of the present invention. By way of example onlyand not limitation, a rule for a variance condition may comprise apredetermined decline in a current measure over a period of time. Inaddition, a rule for a variance condition may comprise a predetermineddifference between a current measure and one of an optimal measure,benchmark measure, and target measure. Generally, any number of rulesmay be defined for a particular clinical facility for monitoring itscurrent clinical process for variance conditions.

Data required to determine if the variance condition is present is nextobtained, as shown at block 1904. The knowledge manager may determinewhat data is required based on the rule previously accessed. Typically,the data will comprise one or more current measures for determiningwhether the particular variance condition being evaluated is present.The knowledge manager may access the clinically-related data from theclinical facility, from a data warehouse, or other associated database.

Comparing the accessed data against the rule for the variance condition,the knowledge manager may determine whether the variance condition ispresent, as shown at block 1906. The determination process is typicallya continual process. Accordingly, if the variance condition isdetermined not to be present at block 1906, the determination processmay be repeated, as represented by the return to block 1902.Alternatively, if the variance condition is determined at block 1906, anindication of the presence of the variance condition is provided, asshown at block 1908. In addition, user interfaces may be generated andprovided to a user for the determination of a root cause of the variancecondition. The user interfaces may utilize clinically-related datacorresponding with the data used to determine whether the variancecondition was present.

Further embodiments of the present invention may be employed to measureand evaluate performance improvements that have been realized for aclinical process. Performance improvements may be identified bycomparing current measures for a particular clinical process againstprevious current measures, which operate as a baseline for purposes ofimprovement evaluation. For example, measures for critical levers for aclinical process for a first period of time may be set as the baseline.Current measures from a subsequent period of time may be comparedagainst this baseline to measure the performance improvements that havebeen realized for the clinical process.

Accordingly, referring to FIG. 20, a flow diagram is providedillustrating an exemplary method 2000 for measuring performanceimprovement for a clinical process within one or more healthcarefacilities in accordance with an embodiment of the present invention.The process may begin at block 2002 when a knowledge manager accesses acurrent measure for a critical lever (i.e., an activity). At block 2004,the knowledge manager accesses a baseline measure for that particularcritical lever. As indicated above, the baseline measure comprises aprevious current measure for the critical lever. The current measure iscompared against the baseline measure to determine a change in thecritical lever, as shown at block 2006. The knowledge manager accessesthose instances (e.g., number of cases or patients) corresponding withthe critical lever, as shown at block 2008. Additionally, optimizedpractice process model data, such as benefit metrics, is accessed, asshown at block 2010. The performance improvement is then determined byapplying the instances and the benefit metrics to the change in thecritical lever, as shown at block 2012. In embodiments in which eachopportunity comprises multiple critical levers, the performanceimprovement for an opportunity may be determined by aggregating theperformance improvements determined for the critical levers comprisingthe opportunity.

An example of the determination of a performance improvement within aclinical process may be discussed with reference to FIG. 21, whichillustrates an exemplary user interface 2100 showing performanceimprovements for a selected area of a clinical process. Thedetermination of performance improvement is discussed herein withrespect to financial benefits; however, in various embodiments of thepresent invention, performance improvement may be measured with respectto non-financial considerations, such as clinical, operational, andregulatory considerations, for example. As shown in FIG. 21, a currentmeasure 2102 and baseline measure 2104 are indicated for each of thelisted critical levers. In addition, the actual benefit (i.e. financialperformance improvement) that has been realized for each of severalopportunities is provided. For example, an actual benefit of $2500 isshown for “Medical Clearance.” This benefit has been realized withrespect to the measurement “% of TKA cases cancelled within 24 hours ofOR date.” As shown in FIG. 21, this measurement has decreased from abaseline measure of 7.5% to a current measure of 5%. Accordingly, if thenumber of cases for the clinical facility is 1000 cases, 25 fewer caseswere cancelled within 24 hours of an OR date. If each case cancelledwithin 24 hours of an OR date creates a financial cost of $100 (a metricthat may be defined within the optimized practice process model), theperformance improvement has resulted in an actual benefit of $2500, asshown in FIG. 21.

As can be understood, the present invention provides systems, methods,and graphical user interfaces for identifying, analyzing, and adoptingopportunities for clinical process optimization based on optimizedpractice process models. The present invention has been described inrelation to particular embodiments, which are intended in all respectsto be illustrative rather than restrictive. Alternative embodiments willbecome apparent to those of ordinary skill in the art to which thepresent invention pertains without departing from its scope.

From the foregoing, it will be seen that this invention is one welladapted to attain all the ends and objects set forth above, togetherwith other advantages which are obvious and inherent to the system andmethod. It will be understood that certain features and subcombinationsare of utility and may be employed without reference to other featuresand subcombinations. This is contemplated and within the scope of theclaims.

1. A method in a clinical computing environment for analyzing areturn-on-investment for one or more opportunities for improving acurrent clinical process within one or more clinical facilities, themethod comprising: accessing optimized practice process model datadefining one or more opportunities for clinical process improvementbased on an optimal process flow, the optimized practice process modeldata further defining return-on-investment metrics for quantifying areturn-on-investment for adopting at least one of the one or moreopportunities; accessing clinically-related data for the currentclinical process based on the one or more opportunities defined by theoptimized practice process model data; and determining areturn-on-investment for adopting at least one of the one or moreopportunities within the current clinical process, thereturn-on-investment being determined based on the clinically-relateddata and return-on-investment metrics defined by the optimized practiceprocess model data.
 2. The method of claim 1, wherein each of the one ormore opportunities is associated with one or more critical levers withinan optimal process flow for a type of clinical process correspondingwith the current clinical process.
 3. The method of claim 1, wherein thereturn-on-investment metrics are associated with each of the one or morecritical levers within the optimized practice process model.
 4. Themethod of claim 1, wherein accessing the optimized practice processmodel data comprises accessing the optimized practice process model datafrom an optimized practice process model database.
 5. The method ofclaim 1, wherein accessing clinically-related data from the currentclinical process comprises accessing the clinically-related data from adata warehouse.
 6. The method of claim 1, wherein the optimized practiceprocess model data further defines each of the one or more opportunitiesfor clinical process improvement as at least one of a clinicalopportunity, a financial opportunity, an operational opportunity, and aregulatory opportunity.
 7. The method of claim 1, wherein thereturn-on-investment metrics comprises benefit metrics, wherein thebenefit metrics facilitate the quantification of a benefit that may berealized by adopting each of the one or more opportunities.
 8. Themethod of claim 7, wherein determining a return-on-investment foradopting at least one of the one or more opportunities comprises:determining a benefit index for at least one of the one or moreopportunities based on the clinically-related data and benefit metricsdefined by the optimized practice process model.
 9. The method of claim1, wherein the return-on-investment metrics comprises effort metrics,wherein the effort metrics facilitate the quantification an effort toadopt each of the one or more opportunities.
 10. The method of claim 9,wherein determining a return-on-investment for adopting at least one ofthe one or more opportunities comprises: determining an effort index forat least one of the one or more opportunities based on theclinically-related data and effort metrics defined by the optimizedpractice process model.
 11. One or more-computer-readable media havingcomputer-useable instructions embodied thereon for causing a computingdevice to perform the method of claim
 1. 12. A computerized system in aclinical environment for facilitating analysis of a return-on-investmentfor one or more opportunities for improving a current clinical processwithin one or more clinical facilities, the system comprising: a firstinterface to a database storing optimized practice process model data,the optimized practice process model data defining one or moreopportunities for clinical process improvement based on an optimalprocess flow, the optimized practice process model data further definingreturn-on-investment metrics for quantifying a return-on-investment foradopting at least one of the one or more opportunities; a secondinterface to a data store storing clinically-related data from thecurrent clinical process; and a knowledge manager communicating with thedatabase via the first interface and the data store via the secondinterface, the knowledge manager configured to determine areturn-on-investment for adopting at least one of the one or moreopportunities within the current clinical process, thereturn-on-investment being determined based on the clinically-relateddata and return-on-investment metrics defined by the optimized practiceprocess model.
 13. The system of claim 12, wherein each of the one ormore opportunities is associated with one or more critical levers withinan optimal process flow for a type of clinical process correspondingwith the current clinical process.
 14. The system of claim 12, whereinthe optimized practice process model data further defines each of theone or more opportunities for clinical process improvement as at leastone of a clinical opportunity, a financial opportunity, an operationalopportunity, and a regulatory opportunity.
 15. The system of claim 12,wherein the return-on-investment metrics comprises benefit metrics,wherein the benefit metrics facilitate the quantification of a benefitthat may be realized by adopting each of the one or more opportunities.16. The system of claim 14, wherein the knowledge manager determines areturn-on-investment for adopting at least one of the one or moreopportunities by determining a benefit index for at least one of the oneor more opportunities based on the clinically-related data and benefitmetrics defined by the optimized practice process model.
 17. The systemof claim 12, wherein the return-on-investment metrics comprises effortmetrics, wherein the effort metrics facilitate the quantification aneffort to adopt each of the one or more opportunities.
 18. The system ofclaim 17, wherein the knowledge manager determines areturn-on-investment for adopting at least one of the one or moreopportunities by determining an effort index for at least one of the oneor more opportunities based on the clinically-related data and effortmetrics defined by the optimized practice process model.
 19. A method ina clinical computing environment for analyzing a return-on-investmentfor one or more opportunities for improving a current clinical processwithin one or more clinical facilities, the method comprising: accessingoptimized practice process model data defining one or more criticallevers based on an optimal clinical process, the optimized practiceprocess model data further associating return-on-investment metrics witheach of the one or more critical levers; accessing one or more currentmeasures for the current clinical process, each of the one or morecurrent measures corresponding with at least one of the one or morecritical levers; and identifying one or more opportunities for improvingthe current clinical process by comparing at least one of the one ormore current measures against the optimized practice process model data;and determining a return-on-investment for adopting at least one of theone or more opportunities within the current clinical process, thereturn-on-investment being determined based on the one or more currentmeasures and the return-on-investment metrics defined by the optimizedpractice process model.
 20. The method of claim 19, wherein theoptimized practice process model data comprises at least one of anoptimal measure, a benchmark measure, and a target measure for at leastone of the one or more critical levers.
 21. The method of claim 20,wherein identifying one or more opportunities for improving the currentclinical process comprises comparing at least one of the one or morecurrent measures against at least one of an optimal measure, a benchmarkmeasure, and a target measure for at least one of the one or morecritical levers.
 22. The method of claim 19, wherein thereturn-on-investment metrics comprises benefit metrics.
 23. The methodof claim 22, wherein determining a return-on-investment for adopting atleast one of the one or more opportunities comprises: determining abenefit index for at least one of the one or more opportunities based onat least one of the one or more current measures and benefit metricsdefined by the optimized practice process model.
 24. The method of claim19, wherein the return-on-investment metrics comprises effort metrics.25. The method of claim 24, wherein determining a return-on-investmentfor adopting at least one of the one or more opportunities comprises:determining an effort index for at least one of the one or moreopportunities based on at least one of the one or more current measuresand effort metrics defined by the optimized practice process model. 26.One or more computer-readable media having computer-useable instructionsembodied thereon for causing a computing device to perform the method ofclaim 19.